September 5, 2025BMJ OpenOpen Access

Factors associated with admission to elderly medical-welfare facilities in South Korea: a cross-sectional machine-learning study

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Discussion

Cross-sectional analysis identifies admissions associated with long-term care needs, highlighting community care importance.

Admission to elderly medical-welfare facilities is significantly influenced by the absence of informal care support, with serious health declines noted.
The final random forest model achieved a sensitivity of 0.856 and a balanced accuracy of 0.921, demonstrating strong predictive power.
Cross-sectional secondary data analysis utilized a large-scale public dataset representative of South Korea's elderly population.
Findings emphasize the necessity for policy interventions to enhance community-based integrated care and support for family caregivers.

Lim et al. (2025) studied this question.

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September 5, 2025BMJ OpenOpen Access

View Full Paper

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Authors

Cross-sectional analysis identifies admissions associated with long-term care needs, highlighting community care importance.

Admission to elderly medical-welfare facilities is significantly influenced by the absence of informal care support, with serious health declines noted.
The final random forest model achieved a sensitivity of 0.856 and a balanced accuracy of 0.921, demonstrating strong predictive power.
Cross-sectional secondary data analysis utilized a large-scale public dataset representative of South Korea's elderly population.
Findings emphasize the necessity for policy interventions to enhance community-based integrated care and support for family caregivers.

Lim et al. (2025) studied this question.

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